307 lines
12 KiB
Python
307 lines
12 KiB
Python
"""
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MAPPO agent for SUMO VSL.
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This implementation uses parameter sharing across edge-agents:
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- Actor: decentralized, one shared policy over per-edge local observations
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- Critic: centralized, one value head over the global state
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"""
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from typing import Dict, Tuple
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class SharedActor(nn.Module):
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def __init__(self, local_obs_dim: int, num_actions: int, hidden_dim: int = 256):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(local_obs_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, num_actions),
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)
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self._init_weights()
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def _init_weights(self):
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for module in self.modules():
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if isinstance(module, nn.Linear):
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nn.init.orthogonal_(module.weight, gain=np.sqrt(2))
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nn.init.constant_(module.bias, 0)
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nn.init.orthogonal_(self.net[-1].weight, gain=0.01)
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def forward(self, local_obs: torch.Tensor) -> torch.Tensor:
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return self.net(local_obs)
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class CentralizedCritic(nn.Module):
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def __init__(self, state_dim: int, hidden_dim: int = 256):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(state_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, 1),
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)
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self._init_weights()
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def _init_weights(self):
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for module in self.modules():
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if isinstance(module, nn.Linear):
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nn.init.orthogonal_(module.weight, gain=np.sqrt(2))
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nn.init.constant_(module.bias, 0)
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nn.init.orthogonal_(self.net[-1].weight, gain=1.0)
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def forward(self, state: torch.Tensor) -> torch.Tensor:
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return self.net(state)
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class MAPPOAgent:
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"""Parameter-sharing MAPPO for edge-wise VSL control."""
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def __init__(
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self,
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state_dim: int,
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num_agents: int,
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num_actions: int,
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edge_feature_dim: int = 3,
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time_feature_dim: int = 3,
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hidden_dim: int = 256,
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critic_hidden_dim: int = 256,
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learning_rate: float = 3e-4,
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gamma: float = 0.99,
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gae_lambda: float = 0.95,
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clip_epsilon: float = 0.2,
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value_coef: float = 0.5,
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entropy_coef: float = 0.01,
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max_grad_norm: float = 0.5,
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ppo_epochs: int = 4,
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minibatch_size: int = 15,
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device: str = "cuda",
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lr_schedule: str = "cosine",
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total_episodes: int = 300,
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):
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self.device = torch.device(device if torch.cuda.is_available() else "cpu")
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self.state_dim = state_dim
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self.num_agents = num_agents
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self.num_actions = num_actions
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self.edge_feature_dim = edge_feature_dim
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self.time_feature_dim = time_feature_dim
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self.gamma = gamma
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self.gae_lambda = gae_lambda
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self.clip_epsilon = clip_epsilon
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self.value_coef = value_coef
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self.entropy_coef = entropy_coef
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self.max_grad_norm = max_grad_norm
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self.ppo_epochs = ppo_epochs
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self.minibatch_size = minibatch_size
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self.speed_feature_dim = 1
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self.last_reward_dim = 1
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self.agent_id_dim = 1
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self.local_obs_dim = (
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edge_feature_dim
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+ self.speed_feature_dim
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+ time_feature_dim
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+ self.last_reward_dim
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+ self.agent_id_dim
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)
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self.actor = SharedActor(self.local_obs_dim, num_actions, hidden_dim).to(self.device)
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self.critic = CentralizedCritic(state_dim, critic_hidden_dim).to(self.device)
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self.optimizer = optim.Adam(
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list(self.actor.parameters()) + list(self.critic.parameters()),
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lr=learning_rate,
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eps=1e-5,
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)
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if lr_schedule == "cosine":
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self.scheduler = optim.lr_scheduler.CosineAnnealingLR(
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self.optimizer, T_max=total_episodes, eta_min=learning_rate * 0.1
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)
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else:
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self.scheduler = None
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agent_ids = np.linspace(0.0, 1.0, num_agents, dtype=np.float32)
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self.agent_id_features = torch.tensor(agent_ids, device=self.device).view(1, num_agents, 1)
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self.reset_buffers()
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def reset_buffers(self):
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self.states = []
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self.actions = []
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self.rewards = []
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self.values = []
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self.log_probs = []
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self.dones = []
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def _build_local_obs(self, state_tensor: torch.Tensor) -> torch.Tensor:
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if state_tensor.dim() == 1:
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state_tensor = state_tensor.unsqueeze(0)
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batch_size = state_tensor.size(0)
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edge_block = self.num_agents * self.edge_feature_dim
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speed_block_start = edge_block
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speed_block_end = speed_block_start + self.num_agents
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global_block_start = speed_block_end
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global_block_end = global_block_start + self.time_feature_dim + self.last_reward_dim
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edge_features = state_tensor[:, :edge_block].view(batch_size, self.num_agents, self.edge_feature_dim)
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local_speed_limits = state_tensor[:, speed_block_start:speed_block_end].view(batch_size, self.num_agents, 1)
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global_features = state_tensor[:, global_block_start:global_block_end].unsqueeze(1)
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global_features = global_features.expand(-1, self.num_agents, -1)
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agent_ids = self.agent_id_features.expand(batch_size, -1, -1)
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return torch.cat([edge_features, local_speed_limits, global_features, agent_ids], dim=-1)
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def select_action(
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self, state: np.ndarray, deterministic: bool = False
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) -> Tuple[np.ndarray, np.ndarray, float]:
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state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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local_obs = self._build_local_obs(state_tensor)
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with torch.no_grad():
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logits = self.actor(local_obs.view(self.num_agents, self.local_obs_dim))
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logits = logits.view(1, self.num_agents, self.num_actions)
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value = self.critic(state_tensor)
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actions = []
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log_probs = []
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for agent_idx in range(self.num_agents):
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dist = torch.distributions.Categorical(logits=logits[0, agent_idx])
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if deterministic:
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action = torch.argmax(logits[0, agent_idx], dim=-1).item()
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else:
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action = dist.sample().item()
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actions.append(action)
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log_probs.append(dist.log_prob(torch.tensor(action, device=self.device)).item())
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return np.array(actions, dtype=np.int64), np.array(log_probs, dtype=np.float32), value.item()
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def get_value(self, state: np.ndarray) -> float:
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state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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with torch.no_grad():
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value = self.critic(state_tensor)
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return value.item()
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def store_transition(self, state, action, reward, value, log_prob, done):
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self.states.append(state)
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self.actions.append(action)
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self.rewards.append(reward)
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self.values.append(value)
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self.log_probs.append(log_prob)
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self.dones.append(done)
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def compute_gae(self, next_value: float):
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advantages = []
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gae = 0.0
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for t in reversed(range(len(self.rewards))):
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if t == len(self.rewards) - 1:
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next_val = next_value
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else:
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next_val = self.values[t + 1]
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delta = self.rewards[t] + self.gamma * next_val * (1 - self.dones[t]) - self.values[t]
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gae = delta + self.gamma * self.gae_lambda * (1 - self.dones[t]) * gae
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advantages.insert(0, gae)
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advantages = np.array(advantages, dtype=np.float32)
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returns = advantages + np.array(self.values, dtype=np.float32)
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return advantages, returns
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def update(self, next_value: float) -> Dict[str, float]:
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if len(self.states) == 0:
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return {}
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advantages, returns = self.compute_gae(next_value)
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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states = torch.FloatTensor(np.array(self.states)).to(self.device)
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actions = torch.LongTensor(np.array(self.actions)).to(self.device)
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old_log_probs = torch.FloatTensor(np.array(self.log_probs)).to(self.device)
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advantages_t = torch.FloatTensor(advantages).to(self.device)
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returns_t = torch.FloatTensor(returns).to(self.device)
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total_policy_loss = 0.0
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total_value_loss = 0.0
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total_entropy = 0.0
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update_count = 0
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dataset_size = len(self.states)
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for _ in range(self.ppo_epochs):
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indices = np.random.permutation(dataset_size)
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for start_idx in range(0, dataset_size, self.minibatch_size):
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end_idx = min(start_idx + self.minibatch_size, dataset_size)
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batch_idx = indices[start_idx:end_idx]
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batch_states = states[batch_idx]
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batch_actions = actions[batch_idx]
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batch_old_lp = old_log_probs[batch_idx]
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batch_adv = advantages_t[batch_idx]
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batch_ret = returns_t[batch_idx]
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batch_local_obs = self._build_local_obs(batch_states)
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logits = self.actor(
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batch_local_obs.view(len(batch_idx) * self.num_agents, self.local_obs_dim)
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).view(len(batch_idx), self.num_agents, self.num_actions)
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dist = torch.distributions.Categorical(logits=logits)
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new_log_probs = dist.log_prob(batch_actions)
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entropy = dist.entropy().mean()
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expanded_adv = batch_adv.unsqueeze(1).expand(-1, self.num_agents)
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ratio = torch.exp(new_log_probs - batch_old_lp)
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surr1 = ratio * expanded_adv
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surr2 = torch.clamp(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon) * expanded_adv
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policy_loss = -torch.min(surr1, surr2).mean()
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values = self.critic(batch_states).squeeze(-1)
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value_loss = nn.functional.mse_loss(values, batch_ret)
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loss = policy_loss + self.value_coef * value_loss - self.entropy_coef * entropy
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self.optimizer.zero_grad()
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loss.backward()
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nn.utils.clip_grad_norm_(
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list(self.actor.parameters()) + list(self.critic.parameters()),
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self.max_grad_norm,
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)
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self.optimizer.step()
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total_policy_loss += policy_loss.item()
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total_value_loss += value_loss.item()
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total_entropy += entropy.item()
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update_count += 1
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if self.scheduler is not None:
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self.scheduler.step()
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self.reset_buffers()
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return {
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"policy_loss": total_policy_loss / max(update_count, 1),
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"value_loss": total_value_loss / max(update_count, 1),
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"entropy": total_entropy / max(update_count, 1),
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}
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def save(self, path: str):
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torch.save(
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{
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"actor_state_dict": self.actor.state_dict(),
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"critic_state_dict": self.critic.state_dict(),
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"optimizer_state_dict": self.optimizer.state_dict(),
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},
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path,
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)
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def load(self, path: str):
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checkpoint = torch.load(path, map_location=self.device, weights_only=False)
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self.actor.load_state_dict(checkpoint["actor_state_dict"])
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self.critic.load_state_dict(checkpoint["critic_state_dict"])
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self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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